The Basics of Logistic Regression in Data Science

Data science has seen a lot of growth in the past few years. The proliferation of data, advanced computing, and cost-effective methods have all contributed to its development. Machine learning algorithms help predict the outcome based on the data fed into it.

Generally, there are two categories of algorithms: classification and regression. In regression, the predicted values are continuous, while in classification, predicted values are categorical. Logistic regression is a classification algorithm.

In this blog, you will learn about logistic regression in detail. If you want to learn more about the concept of classification and regression, consider going to a reputed data science bootcamp.

What is Logistic Regression?

It is a common type of analysis method used by data professionals. It predicts an outcome after observing the dataset. This method is useful in predicting the result if there are only two possible outcomes.

For example, logistic regression will apply to a situation where you use the data about a party’s previous performance and the opposition’s performance to predict if a party will win an election or not, as there are only two probable results here: success/defeat.

The outcomes received from logistical regression help businesses in many ways, improving their ROI, achieving business goals, enhancing marketing strategies, and more.

Types Of Logistic Regression

Till now, What you have understood about logistic regression is having only two possible outcomes/categories, which is binary logical regression. But logistic regression has two more types: Multinomial and Ordinal logistic regression. Let’s understand all the three types of logistic regression in detail below.

· Binary logistic Regression: It establishes a connection between a dependent and an independent variable. The dependent variable is binary, and the output is considered to fall into one of the two categories, for example, male/female, yes/no, success/failure, pass/fail, win/lose, etc.

· Multinomial logistic Regression: This is somewhat similar to binary logistic regression and can be seen as its extension. Instead of one possible outcome as in the binary logistic regression, the dependent variable can have more than two outcomes. So, let’s assume if you want to predict the most popular foods this year. The food will be the dependent variable with possible outcomes of, for example, hard seltzer, boba, birria, seafood boil, sushi, and fried chicken.

· Ordinal logistic Regression: It is used to predict a dependent variable with ordered/ordinal categories. There is a natural and meaningful order in the categories. Examples of ordered variables in the dependent variable are a survey with responses such as strongly disagree/ disagree, agree/ strongly agree, scores such as poor/average/good/excellent, etc.

You need to go through data science training to know when and how to apply logistic regression. Some of the real-life situations where logistic regression is used are:

Applications of Logistic Regression In Real Life

· How weight and diet can impact the possibility of a heart attack

· How a GPA score affects the chance of a student getting selected or rejected

· If an email is spam or not

· How transaction amount and credit score impact the likelihood of a transaction being fraudulent

· If a tumor is fatal or not based on the size of the tumor and affected area, etc.

· A person will buy a car or not

· A contestant will win or lose an election

· Users will buy or not a product

· Users will like a suggestion or news feed or not

So, these were some examples where Logistic regression is useful. There are many reasons it is popular in data science coding. Let’s look at some of them below.

Benefits Of Using Logistic Regression

Easy to implement

Compared to other methods, especially in the machine learning context, it is far easier to implement and offers great training efficiency. Also, you don’t need high computational power when you’re training your model with this algorithm. The training time is also less compared to other complex algorithms.

Provide Probability Predictions

Logistic regression offers probability prediction and not just classification labels as you see in other ML algorithms like kNN. It also gives inference about the accuracy of the training model. In addition, you also integrate it with other systems working on probability measures.

Useful in Linearly Separable Datasets

A linearly separable dataset means it can be separated into two classes of data. In such cases, logistic regression is ideal as you can efficiently classify data into two separate classes. Like you can classify the gender variable as male or female.

Offers Useful Insights

Logistic regression measures the relevancy of an independent variable and also if the relationship is positive or negative. Positive relation is when an increase in one variable increases another variable, and negative is when an increase decreases another variable. You get better at something by putting more hours into practice; it indicates a positive relationship. One thing you should know is that logistic regression shows a correlation but not causation. So, there can be a relation between these two actions, but it does imply that one action caused the other.

Scales With Large Data

Logistic regression is a quick and efficient algorithm that adjusts with large datasets. Unlike algorithms like SVMs, kNNs, which struggle on large datasets, logistic regression can work really well with huge datasets and a large number of features.

Model Flexibility

You can fine-tune the fitting or reduce the errors in the model by applying the regularization techniques to logistic regression. You can try the Ridge, Lasso, or Elasticnet regularization models. Regularization will make it less prone to noise and outliers.

Conclusion

Logical regression is a classic machine learning method suited for classification problems, where the dependent/output variable is categorical or dichotomous. It helps with probability and classification problems. It lies at the core of machine learning along with linear regression, principal component analysis, k-mean clustering, and more.

If you are thinking of a data science career, you must grasp all the essential concepts. A data science bootcamp will provide you the necessary training in a structured way. It will familiarize you with all the data science tools and techniques to help you launch a data science career.

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